Incomplete Label Distribution Learning by Exploiting Global Sample Correlation

In recent years, label distribution learning (LDL) has become a new learning paradigm in the field of machine learning. LDL is mainly designed to solve the problem of ambiguity among labels. Although LDL has been successful in many applications, most of these efforts are centered around complete supervised information. However, in reality, the supervised information is often incomplete due to the huge cost of label annotation. To address this problem, this paper proposes a novel incomplete LDL approach by utilizing the global sample correlation (IncomLDL-GSC). The label correlation is also considered to improve the performance of the model. Extensive experiments are conducted on 13 data sets to demonstrate the effectiveness of our proposed method.